Cluster Computing

, Volume 22, Supplement 5, pp 12713–12725 | Cite as

Redundant placement of phasor measurement unit using multi objective evolutionary algorithms based on modified spectral clustering

  • Nafeena RahamathullahEmail author
  • Willjuice Iruthayarajan Mariasiluvairaj


Optimal placement of phasor measurement unit (OPPMU) considering controlled islanding is a high dimensional constrained optimization problem that determines optimum location of PMU. Controlled islanding can be used as an effective emergency action to split a large scale power system into islands for avoiding blackouts. This paper proposes an optimal PMU placement model considering power system controlled islanding. So that the power network remains observable under controlled islanding as well as normal operating condition. A new method based on spectral clustering is proposed to handle multiple constraints in order to guarantee the stability of generated islands. The objective function used in this controlled islanding algorithm is the minimal power flow disruption. Constraints such as generator coherency, load–generation imbalance are considered for spectral clustering. OPPMU is formed for the obtained islands using two conflicting objectives of minimizing the number of PMU and maximizing the measurement redundancy using conventional mixed integer linear programming, real coded genetic algorithm(RGA), non dominated sorting differential evolutionary algorithm, and modified non dominated sorting GA II (MNSGA II). RGA with simulated binary crossover and non dominated sorting differential evolution with improved crowding distance and mutation is implemented. In MNSGA II proposed a variant which reduces the run-time complexity using the technique space–time-trade-off. Simulation results using NE 39 and IEEE 118 bus test systems show that the proposed method is computationally efficient when solving controlled islanding based OPPMU problem.


Phasor measurement unit Evolutionary computation Clustering Redundancy Islanding 


  1. 1.
    Wilson, R.J.: Introduction to Graph Theory. Longman Group Ltd. ISBN 0-582-24993-7 (1996)Google Scholar
  2. 2.
    Sun, K., Zheng, Z., Lu, Q.: Splitting strategies for islanding operation of large scale power systems using OBDD-based methods. IEEE Trans. Power Syst. 18(2), 912–923 (2003).
  3. 3.
    Yang, B., Vital, V., Heydt, G.T.: Slow coherency based controlled islanding—a demonstration of the approach on the August 14, 2003 Blackout Scenario. IEEE Trans. Power Syst. 21, 1840–1847 (2006).
  4. 4.
    Sun, K., Zheng, D.Z., Lu, Q.: A simulation study of OBDD-based proper splitting strategies for power systems under consideration of transient stability. IEEE Trans. Power Syst. 20(1), 389–399 (2005).
  5. 5.
    Xu, G., Vittal, V., Meklin, A., Thalman, J.E.: Controlled islanding demonstrations on the WECC system. IEEE Trans. Power Syst. (2011).
  6. 6.
    Ding, T., Sun, K., Huang, C.: Mixed Integer linear programming based splitting strategies for power system islanding operation considering network connectivity. IEEE Syst. J. (2015).
  7. 7.
    Gomez, O., Rios, M.A.: Real time identification of coherent groups for controlled islanding based on graph theory. IET J. Gener. Transm. Distrib. 9(8), 748–758 (2015).
  8. 8.
    Ding, L., Ma, Z., Wall, P.: Graph spectra based controlled islanding for low inertia power systems. IEEE Trans. Power Deliv. 32(1), 302–309 (2017).
  9. 9.
    Fang, N., Zhou, J.: A crossover of real coded genetic and artificial fish swarm algorithm for short term optimal hydrothermal scheduling. Int. J. Electr. Power Energy Syst. 62, 617–629 (2014).
  10. 10.
    Aghaie, M., Norouzi, A., Zolfaghari, A.: Advanced progressive real coded genetic algorithm for nuclear system availability optimization through preventive maintenance scheduling. Int. J. Ann. Nucl. Energy 60, 64–72 (2013).
  11. 11.
    Juang, J.-G., Huang, M.-T., Liu, W.-K.: PID control using preresearched genetic algorithm for MIMO system. IEEE Trans. Syst. Man Cybern. C (2008).
  12. 12.
    Jensen, M.T.: Reducing the run time complexity of multi objective EAs. The NSGAII and other algorithms. IEEE Trans. Evol. Comput. 7, 502–512 (2003)Google Scholar
  13. 13.
    Huang, L., Sun, Y., Xu, J., Gao, W., Zhang, J., Wu, Z.: Optimal PMU placement considering controlled islanding of power systems. IEEE Trans. Power Syst. (2014).
  14. 14.
    Ahmed, S.S., Sarker, N.C., Khairuddin, A.B., Ghani, M.R.B.A., Ahmad, H.: A scheme for controlled islanding to prevent subsequent blackout. IEEE Trans. Power Syst. 18(1), 136–143 (2003).
  15. 15.
    Chow, J.H.: Time-Scale Modeling of Dynamic Matrix with Applications to Power Systems. Springer, New York (1982).
  16. 16.
    Milosevic, B., Begovic, M.: Nondominated sorting genetic algorithm for optimal phasor measurement unit placement. IEEE Trans. Power Syst. 18(1), 69–75 (2003).
  17. 17.
    Velmurugan, T., Santhanam, T.: Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distortions of data. J. Comput. Sci. 6(3), 363–368 (2010).
  18. 18.
    Henner, V.E.: A matrix separation scheme for emergency control. Int. J. Electr. Power Energy Syst. 2(2), 109–114 (1980).
  19. 19.
    Phadke, A.: Synchronized phasor measurements in power systems. IEEE Comput. Appl. Power (1993).
  20. 20.
    Esmail, M., Gharani, K., Shayanfar, H.A.: Redundant observability PMU placement in the presence of flow measurements considering contingencies. IEEE Trans. Power Syst. (2013).
  21. 21.
    Nafeena, R., Iruthayarajan, M.W.: Redundant location of PMU for complete observability and wide area monitoring of power system using real coded genetic algorithm. Asian J. Res. Soc. Sci. Humanit. 6, 1131–1145 (2016).
  22. 22.
    Ahmadi, A., Alinejad-Beromi, Y., Moradi, M.: Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. Int. J. Expert Syst. Appl. 38, 7263–7269 (2011).
  23. 23.
    Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001).
  24. 24.
    Deb, K., Kumar, A.: Real coded genetic algorithm with simulated binary crossover: studies on multi modal and multi objective problems. Complex Syst. 9, 431–454 (1995).
  25. 25.
    Storn, R., Price, K.A.: A simple and heuristic for global optimization over continuous spaces. ACM J. Global Optim. Syst. 11, 341–359 (1997).
  26. 26.
    Iruthayarajan, M.W., Baskar, S.: Evolutionary algorithm based design of multivariable PID controller. Int. J. Expert Syst. Appl. 36, 9159–9167 (2009).
  27. 27.
    Sun, H.J., Peng, C.H., Guo, J.F.: Non-dominated sorting differential evolution algorithm for multi-objective integrated generation bidding and scheduling. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 4, 20–22 November 2009.
  28. 28.
    D’Souza, R.G.L., Chandra Sekaran, K., Kandaswamy, A.: Improved NSGA-II based on a novel ranking scheme. J. Comput. 2(2). ISSN 2151-9617 (2010).

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Nafeena Rahamathullah
    • 1
    Email author
  • Willjuice Iruthayarajan Mariasiluvairaj
    • 1
  1. 1.National Engineering CollegeKovilpattiIndia

Personalised recommendations